There is an increasing demand for photo-realistic modeling of buildings and cities for applications including three-dimensional (“3D”) map services, games, and movies. The modeling of buildings and cities often reduces de facto to that of building facades. The current state of the art ranges from pure synthetic methods based on grammar rules and 3D scanning of street facades, to image-based approaches which utilize either small or large numbers of images. This includes the problem of detecting translational repetitive patterns in an orthographic image, where there have been several methods for regularity or symmetry analysis. These include Hough-transform like methods, varying in the manner on how to sample data space and where to vote. A recent framework discovers structural regularity in 3D geometry using discrete groups of transformations, for example. The following provides a brief non-exhaustive discussion of architectural modeling techniques including symmetry detection, façade modeling, and inverse procedural modeling.
Symmetry in 3D techniques often employs a voting scheme in transformation space to detect partial and approximate symmetries of 3D shapes. These include symmetrization applications to discover structural regularity in 3D geometry using discrete groups of transformations. Another application provides a solution by manually creating models from 3D scanners and also relied on manually specifying symmetry patterns. Symmetry in images provides a computational model that detects peaks in an autocorrelation (“AC”) function of images to determine the periodicity of texture patterns. One application mapped a 2D rotation symmetry problem to a Frieze detection problem and employed discrete Fourier transform for Frieze detection. Subsequently, a more general method to detect skewed symmetry was proposed.
Another modeling technique detected and grouped selected elements in a graph structure based on intensity variation. One method identified salient peaks using Gaussian filters to iteratively smooth the AC function, where translation vectors are determined by generalized Hough transforms. Another technique utilized edge detection to determine elements and successively grow patterns in a greedy manner, where a grouping strategy for translational grids based on maximum likelihood estimation. Yet another technique introduced a pair-wise local feature matching algorithm using key points at corresponding locations. One algorithm detects rotation symmetry centers and symmetry pattern from real images, but does not address all symmetry group properties. Another proposal was to test and find the optimal hypotheses using a statistical model comparison framework. There are also methods that analyze near-regularity detection.
Facade modeling includes image-based methods, where images are employed as guides to interactively generate models of architectures. Many vision-based methods require registered multiple images. One algorithm for structure detection in building facades utilized a strong prior knowledge regarding facade structures to detect translational repetitive windows. Another technique relaxed the input image to process strong perspectives, where repeated point features are grouped using chain-wise similarity measurements. Yet another technique employed a priori knowledge regarding grid patterns on building facades which is formulated as Markov Random Field and discovered by Markov Chain Monte Carlo optimization.
Another method utilizes a variation of RANSAC-based planar grouping method to detect perspectively distorted lattices of feature points which allows identification of the main translation vectors of the underlying repeated wallpaper pattern. An interactive system was employed to create a model from a single image by manually assigning the depth based on a painting metaphor. Another system used a sketching approach in one or more images, where yet another system interactively recovered a 3D texture-mapped architecture model from a single image by employing constraints derived from shape symmetries.
Inverse procedural modeling includes L-system approaches for plant modeling which is perhaps the most representative of procedural approaches. Inverse modeling from images to extract rules is also provided for tree modeling. For architecture modeling, Computer Generated Architecture (“CGA”) shape software combined a set grammar with a split rule and produced detailed building geometries. Although the design of grammar systems has been utilized, there is limited work on how to extract grammars from existing models as inverse modeling. One grammar extraction method uses a top-down partition scheme to extract split rules from a rectified facade image. However, extracted grammar rules are limited to grid-like subdivisions. Recent approaches on inverse procedural modeling recognize a vector picture and employ extracted rules to re-synthesize new pictures.
The above-described deficiencies of today's 3D modeling are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with conventional systems and corresponding benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.